Time series clustering has been an important research field in the last decade, providing useful and effective information in diverse domain. As outcome of the great existing interest for part of the scientific community of data mining area, innumerable research works have arisen that propose new algorithms and methodologies to identify cluster in the data time series. To provide an overview, this paper surveys and summarizes works that investigated the data time series clustering in diverse applications field. The basic concepts of time series clustering are presented and the surveyed works are organized into three groups: temporal-proximity-based, model-based and representation-based. The application areas are summarized with a brief description of the used data. The characteristics and particularities of some works are discussed.